Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network
Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network
复制标题
使用无人机和卷积神经网络对建筑物进行自动区域地震损伤评估
DOI：
10.1016/j.autcon.2019.102994
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发表时间：
2020-01-01
影响因子：
10.3
通讯作者：
Lu, Xinzheng
中科院分区：
文献类型：
--
作者：
Xiong, Chen;Li, Qiangsheng;Lu, Xinzheng
快速评估地震对建筑物造成的破坏，有助于改善地震易发地区的应急反应和及时救援。本文介绍了一种基于无人机和卷积神经网络的建筑物地震损伤自动评估方法。该方法包括三个部分：(1)数据准备，(2)建筑图像分割，(3)基于cnn的建筑震害评估。首先，使用三维（3D）建筑模型、航空图像和相机数据进行以下模拟。其次，提出了一种以三维建筑模型为参考的建筑图像分割方法，通过该方法可以获得多视图分割的建筑图像。随后，采用基于VGGNet的CNN模型对各建筑进行震害评估。CNN模型是基于从互联网上获得的人工标记的建筑图像进行微调的。最后，以北川古镇为例，验证了该方法的有效性。得到了该区域的损伤分布，准确率为89.39%。
A rapid assessment of the seismic damage to buildings can facilitate improved emergency response and timely relief in earthquake-prone areas. In this study, an automated building seismic damage assessment method using an unmanned aerial vehicle (UAV) and a convolutional neural network (CNN) is introduced. The method consists of three parts: (1) data preparation, (2) building image segmentation, and (3) CNN-based building seismic damage assessment. First, a three-dimensional (3D) building model, aerial images, and camera data are used for the following simulation. Next, a building image segmentation method is proposed using the 3D building model as georeference, through which multi-view segmented building images can be obtained. Subsequently, a CNN model based on VGGNet is adopted to assess the seismic damage of each building. The CNN model is fine-tuned based on manually tagged building images obtained from the Internet. Finally, a case study of the old Beichuan town is used to demonstrate the effectiveness of the proposed method. The damage distribution of the area is obtained with an accuracy of 89.39%.